Traffic data extraction is in increasing demand for applications such as traffic light control, population evacuation, and for reducing the impact of traffic, including congestion, pollution, delays and accidents.
Magnetic loop technology is widely used for extracting traffic data from a road. The loop detectors are buried underneath the road to provide real-time statistics. These loops are however expensive, cause traffic stoppages for installation, and they are only able to provide data on the presence of a vehicle.
The use of fixed video cameras is more promising as cameras are able to extract a larger range of traffic data. Advanced traffic management systems use computer vision techniques to automatically process the images of the camera's field of view (FOV) and extract traffic data such as vehicle counting, congestion detection, speed estimation of the vehicles and queue length estimation.
Although use of computer vision in traffic surveillance systems is an active research area, current research focuses mainly on algorithms designed for day time conditions.
An efficient but expensive technique to detect vehicles in the night time is to use night vision sensors, such as infrared-thermal cameras. These cameras provide greyscale images where each pixel gives information about the temperature of the object it represents. Unfortunately, the use of infrared sensors in traffic surveillance systems is often prohibitively costly.
Usually, it is more affordable to use common video charged-coupled device (CCD) or a complementary metal-oxide semiconductor (CMOS), camera however CCD and CMOS cameras are more prone to capturing noise in images taken in the night time.